Contexte:

L’institut des systèmes complexes de Paris est une structure de recherche du CNRS interdisciplinaire et un lieu d’échange pour des scientifiques de nombreuses disciplines. Une de ces missions est de développer des outils transversaux pour les systèmes complexes.

Le logiciel libre OpenMOLE est développé depuis 2008 au sein de l’ISC-PIF. Il permet l’exploration et la validation de modèles de simulation scientifique. Son cœur est codé en Scala, et son interface web en Scala-js (Scala compilé vers du javascript). Un démonstrateur de cette application est disponible ici : demo.openmole.org.

OpenMOLE propose de nombreuses méthodes avancées pour l’exploration des modèles de simulation (calibrage automatique, analyse de sensibilité, optimisation multi-objectifs, recherche de diversité, etc). Ces méthodes itératives produisent des résultats bruts à haute information mais ne possèdent pas pour l’heure de représentation graphique ergonomique. Une API fournissant pour chaque méthode la convergence, les meilleurs résultats obtenus depuis le début de la simulation, est en cours de développement.

Missions:

Le stage s’articule en quatre temps :

1) une prise en main des méthodes produisant des données à représenter dans le cadre du stage.
2) la construction sous forme de mockup d’un tableau de bord de l’évolution de la simulation pour les méthodes envisagées.
3) l’implémentation de ces deux tableaux de bord dans une application autonome. Ces implémentions se feront en Scala / Scala-js et utiliseront la librairie graphique Plotly.
4) l’intégration des tableaux de bord dans l’application OpenMOLE

Technologies et compétences à mettre en œuvre durant le stage:

  • Scala / Scala-js
  • Mathématiques niveau licence
  • UX – Expérience Utilisateur
  • Plotly
  • Javascript
  • OS : linux
  • Attrait pour le monde de la recherche et le logiciel libre

Détails:

Durée: 4 à 6 mois

Lieu: Institut des Systèmes Complexes (ISC-PIF) / Paris-13

Niveau: Licence ou Master

Contact : mathieu.leclaire@iscpif.fr, romain.reuillon@iscpif.fr

Stage indemnisé (environ 500€ / mois)

Candidature:

  • CV détaillant les formations reçues et les expériences professionnelles antérieures
  • Lettre de motivation

We are looking for students who are interested in Mobile development, especially Android!

Diploma required: Bac + 5 in a quantitative field (applied mathematics, statistics, computing…)
Internship starting date: Flexible
Duration : 2 – 6 month
Salary policy: the internship is paid according to the legal wage rates (approx. 560€/month)

About the Institute of Complex Systems (ISC-PIF)

Created in 2005, ISC-PIF is a CNRS service and research unit dedicated to the inter-institutional and inter-disciplinary development of research on complex systems. At once a research laboratory, project incubator, shared resource centre, conference centre and academic co-working space, this scientific hub provides researchers with a dynamic research environment and innovative tools based on big data and high-performance computing.

Address :
Institut des Systèmes Complexes de Paris IdF,  113 rue Nationale 75013 Paris

About the internship

Description :

We are looking for students interested in a  2-6 months internship to help us create the equivalent of our existing iOS apps for Android. You are free to use either React Native or Android (Java/Kotlin) or a combination of both.

Some of the existing iOS apps:

These iOS apps have essentially the same structure and features which makes it easy to reuse across the 3 Android apps.

Requirements : 

  • Proficient in Android (Java, Kotlin) or strong knowledge of React.js
  • Experience with third-party libraries and APIs
  • Very comfortable with NoSQL Databases like MongoDB and Elasticsearch
  • Solid understanding and experience with JavaScript, HTML5, CSS3
  • Solid understanding of the full mobile development life cycle
  • (Bonus) Published an app to the Google Play Store
  • (Bonus) Familiarity with REST and Websockets on mobile
  • (Bonus) Experience with the MVVM and/or MVP design patterns

Responsibilities (two or more) :

  • Converting Politoscope iOS app to Android
  • Converting SciCope iOS app to Android
  • Converting 24 News iOS app to Android

How to Apply :

Please email your job application (reference in the subject line: Multivac Intern) including a cover letter, a resume, and an indication of availability date to maziyar dot panahi at iscpif dot fr. 

Multivac Big Data Architecture

 

NOTE: This position is open for the year 2021.

We are looking for students who are interested in Data Science and Machine Learning! A great opportunity to work with cutting-edge technologies and billions of data. You will be working on Multivac Platform developed at ISC-PIF : our platform is one of the biggest academic repositories with over 15 billion documents hosted across 80 servers on dedicated servers and cloud services.

Diploma required: Bac + 5 in a quantitative field (applied mathematics, statistics, computing…)
Internship starting date: Flexible
Duration : 2 – 6 month
Salary policy: the internship is paid according to the legal wage rates (approx. 560€/month)

About the Institute of Complex Systems (ISC-PIF)

Created in 2005, ISC-PIF is a CNRS service and research unit dedicated to the inter-institutional and inter-disciplinary development of research on complex systems. At once a research laboratory, project incubator, shared resource centre, conference centre and academic co-working space, this scientific hub provides researchers with a dynamic research environment and innovative tools based on big data and high-performance computing.

Address :
Institut des Systèmes Complexes de Paris 113 rue Nationale 75013 Paris

About the internship

Description :

You will be working on the Multivac Platform developed at ISCPIF. Multivac Platform is one of the biggest academic repositories with over 75 billion documents hosted across 100 servers on dedicated servers and cloud services. The datasets contain metadata from published scientific papers and social networks with a wide range of topics. Multivac platform is meant as an interface between researchers and Big Data, especially in the domain of NLP and text mining. It offers services such as comprehensive dashboards that enable scientists to explore and discover facts with a wider overview of large-scale data through visualizations. It also offers API access that allows researchers to exploit this huge architecture and computation without any prior technical knowledge. In addition, Multivac Data Science Lab offers interactive notebooks over Apache Hadoop/Spark cluster in private Cloud.

Why Multivac Platform :

Multivac Platform is built by cutting-edge technologies such as:

  • Large-scale databases (MongoDB and Redis with over 12 billion documents)
  • Search engine clusters (Elasticsearch/Kibana with over 6 billion documents)
  • Distributed computations and real-time processing (RabbitMQ, NodeJS, etc.)
  • Cloudera Hadoop 2.0 with interactive Spark notebooks (HDFS, YARN, Apache Spark, Apache Hive, Apache HBase, Apache Zeppelin, Hue, etc.)
  • Cloud services (OpenStack)

You get to learn all about these new technologies and have access to Multivac Data Science Lab. Multivac Platform hosts over 14 billion data with over 50 million data every day.

Multivac Data Science Lab

Multivac Data Science Lab

Requirements : 

  • Master in Statistics or Data Sciences
  • Basic knowledge of Machine Learning Algorithms
  • Good knowledge of Scala, Python, or R
  • Good knowledge of Deep Learning libraries (BigDL, Tensor Flow …)
  • Strong knowledge of text mining in social networks
  • Interest in NLP tasks and Graph analytics
  • Experience with Twitter datasets and other REST API services (Bonus)
  • Familiar with Apache Spark or any other Hadoop components (Bonus)

Responsibilities (two or more) :

  • Work on unsupervised learning algorithms for topic detection
  • Work on supervised learning algorithms for classifications and predictions
  • Develop and optimize our existing LDA implementations
  • Develop algorithms to perform NLP tasks such as clustering, topic detections, etc. (StanfordCoreNLP)
  • Implement algorithms to improve sentimental analysis and mentions clustering
  • Develop and implement methods of automatic detection of opinions in Tweets
  • Implement methods of keyword extractions in scientific publications

How to Apply :

Please email your job application (reference in the subject line: Multivac Intern) including a cover letter, a resume, and an indication of availability date to maziyar dot panahi at iscpif dot fr. 

Sujet du stage

L’urgence écologique, dont nous prenons conscience depuis quelques dizaines d’années maintenant, a entraîné une transformation progressive des modes de mobilité urbaine de proximité vers des solutions qui favorisent les transports publics et les mobilités « douces ». Début 2020 en Europe, la pandémie de COVID-19 vient percuter ces transformations en cours et les individus réévaluent, en un temps très court, leurs options de déplacement entre des modes isolés comme l’automobile ou le vélo personnel, les transports publics, ou encore les services partagés comme le Vélo en Libre Service (VLS) [1].

Des considérations relatives aux risques sanitaires accrus ou aux risques de circulation moindre s’invitent par exemple dans les décisions individuelles. Si le contexte pandémique a dans l’ensemble donné un coup d’accélérateur à l’utilisation des VLS, on peut avancer au vu des premières analyses que cette accélération est différentiée suivant les secteurs urbains et les types de déplacements.

Le défi pour nos sociétés urbaines est maintenant de pérenniser et d’accompagner ces transformations d’usage. Cela passe par une analyse fine des dynamiques à l’œuvre en distinguant en particulier celles qui relèvent déjà du temps long et qui sont dues en particulier à la prise de conscience écologique, de celles qui relèvent de la crise sanitaire et qui ont réellement entraîné une bifurcation (au sens des systèmes complexes) dans la dynamique des usages en cours. Le projet dans lequel s’inscrit ce stage de Master aborde ces questions pour la ville de Toulouse et le cas des VelÔToulouse.

À Toulouse, deux ans après Lyon, c’est en 2007 que JCDecaux met en place les premiers VLS, les “velÔToulouse”, que nous avons aujourd’hui : près de 50 millions de trajets ont été effectués depuis leur mise en fonction, 100 millions de kilomètres parcourus, 284 stations équipées et 2600 vélos sont disponibles. Le projet que nous menons propose de croiser les données d’emprunts des velÔToulouse et des résultats d’enquêtes pour 1) comparer de façon interdisciplinaire la cartographie des usages des Vélos en Libre Service avant, pendant et après les périodes de confinement, 2) questionner l’effet de la pandémie COVID-19 à court et moyen termes sur les transformations d’usages des VLS, 3) penser la perspective d’une intensification de l’utilisation des VLS et de l’extension du réseau vers la périphérie urbaine toulousaine.

Le stage de master s’inscrit essentiellement dans le premier enjeu. Pour cela nous disposons des données d’emprunt des vélÔToulouse, sous la forme de fichier texte, depuis début 2019. Il s’agit d’environ 4 millions de log d’emprunts de velÔToulouse par an.

Le stage de master consistera à reconstruire la dynamique spatio-temporelle des emprunts de velÔToulouse depuis début 2019 en séparant les périodes pre-, pendant- et post-confinement, et à proposer des premières analyses. Les deux périodes de confinement pourront aussi être comparées.

Le stage comportera 4 phases :

  1. mise en place d’un outils automatique d’interrogation des fichiers de log, paramétré par la fenêtre temporelle et la localisation des stations à prendre en compte et qui fournit en sortie un graphe pondéré dont les nœuds sont les stations et les poids des arêtes correspondent au nombre de vélos empruntés à la 1ère station et rendus à la 2ème dans la fenêtre temporelle.
  2. comparaison des graphes de différentes périodes pre-, pendant- et post-confinements à l’aide d’outils classiques d’analyse de réseaux.
  3. implémentation d’une méthode « par flots de liens » [2] pour détecter des bifurcations ou changement de rythme et comparaison avec les résultats du 2).
  4. Tentatives d’interprétations.

[1] Bert, J., Schellong, D., Hagenmaier, M., Hornstein, D., Wegscheider, A. K., & Palme, T. (2020). How COVID- 19 Will Shape Urban Mobility. Boston Consulting Group
[2] Latapy, M., Viard, T. & Magnien, C. (2018). Stream graphs and link streams for the modeling of interactions over time. Soc. Netw. Anal. Min. 8, 61.

Profil recherché : Le stagiaire de master sera accueilli dans notre équipe pluridisciplinaire qui comporte deux mathématiciens, une sociologue, deux urbanistes-géographe, un économiste, une cartographe, et un anthropologue. Le stage s’inscrit dans le cadre d’un partenariat avec la métropole de Toulouse au sein du pôle d’expertise VILAGIL. Le profil que nous recherchons en priorité est un étudiant de master étant sensibilisé aux techniques d’analyse de réseaux et plus généralement de systèmes complexes, ayant de bonnes connaissances en informatique et en traitement automatique des données (Python, R, …), et ayant une appétence pour les approches interdisciplinaires.

Rémunération : Le stage sera d’une durée possible entre 4 et 6 mois et effectué intégralement en 2021. La rémunération est classique d’environ 570€ par mois, suivant le nombre de jours ouvrés.

Lieu de stage : Laboratoire Interdisciplinaire Solidarités, Sociétés, Territoires (LISST), UMR 5193 CNRS EHESS UT2J, Toulouse.

Contact et encadrant : Bertrand Jouve, Directeur de Recherche CNRS. bertrand.jouve@cnrs.fr

Postdoc recruitment to study the gene networks driving ageing

The CRI-Paris currently offers a postdoc position in the areas of Network Science, Network Medicine, and Computational biology applied to the study of Ageing, to be filled 1st January 2021.

The applicant will join the team of Michael Rera (Utelife Lab) at the CRI in co-supervision with Marc Santolini (Interaction Data Lab, CRI) and Anastasios Giovannidis (CNRS, Sorbonne University, LIP6 lab). The project will focus on applying network approaches to understand and model the dynamics of gene networks driving ageing in Drosophila and Humans. In particular, the project will focus on describing ageing as a propagation of network failures in the multi-layer interactome. The project is supported by a French national ANR JCJC funding.

Duration: 24 months

Relevant backgrounds and experience:

• PhD in network science, data science, computer science, computational biology, engineering, physics or other related technical disciplines

• Advanced expertise in the use of Python and/or R and network libraries/packages

• Experience in advanced data wrangling and analysis

• Experience with the mining, analysis and visualisation of large network data

• Familiarity with manipulating large-scale biological data (i.e RNAseq, proteomics, metabolomics…)

Profile:

• Proven problem-solving skills (inquisitive mind and intellectual curiosity)

• Excellent communication, collaboration, and presentation skills

• Proactive and innovative

• Capacity to listen actively, obtain necessary input, share ideas, speak persuasively, and convey information in a clear, objective, and concise manner

• Ability to work in a team-oriented environment, and function productively in a dynamic work environment

• Take initiative, and be persistent in her/his drive for results

• Ability to adapt to changing circumstances

• Ability to breakdown undefined problems into specific, workable components

Income follows the standard salary grid with a gross monthly income of 2620 euros

Interested candidates should submit a formal application to these 3 addresses: Michael Rera , Marc Santolini and Anastasios Giovanidis consisting of (i) a current CV with past experience and programming skills highlighted (preferably with link to Github or equivalent), (ii) a brief statement of research experience and interests (max. 2 pages) and (iii) the contact information of up to two references (e-mail or phone number) with some context information (relationship to applicant). Do not hesitate to contact us for further questions as well!

We believe in community diversity as a driving force of excellence. Therefore, we strongly encourage members of underrepresented groups to apply.

ABOUT THE CRI

The Center for Research and Interdisciplinarity (CRI) experiments and spreads new ways of learning, teaching, conducting research and mobilizing collective intelligence in life, learning and digital sciences. The core mission of the CRI is to transform the way to research and acquire, share and co-create knowledge across the life, learning, and digital sciences. We are building a research collaboratory – up to 60 scientists, postdocs, PhD/master students working closely together on diverse but mutually complementary range of topics. We are guided by UN Sustainable Development Goals towards high-impact work on specific topic combining biomedicine, natural sciences, education, and digital transformation.

More info: https://research.cri-paris.org.

Project Web Page : https://projects.cri-paris.net/projects/tcLBvZjk/summary

Title: IoT-microservices platform for mobility monitoring, analysis and recommendation
Postdoctoral position (12 months – renewable to 18 months)
Primary research field: Software Engineering – Distributed Systems
Secondary research field: Data Analytics – Network Science
Start date: July-December 2019

Program Benefits
================
Net salary: ~2,000-2300 Euros per month. Some additional income can be earned by teaching.
Academic and industrial professional development including travel support.
Interaction with world-renowned external board members and speakers
Travel grant for attending conferences and workshops.

Research Center: University Gustave Eiffel – Campus Lyon, LICIT laboratory
(25 avenue Francois Mitterand, Bron, France).

Postdoc project description
================
The project aims to develop a data-driven monitoring and decision-making platform,
based on IoT and micro-services technologies, to improve transport resilience via
(real-time) big data analytics and complex network mining solutions.

The project provides a unique opportunity to work with a large variety of collected
real-world large-scale datasets for the city of Lyon, France from our partners,
including:

  • mobile phone (passive) data,
  • multi-modal transport network of the city of Lyon,
  • GPS floating car data,
  • survey data,
  • vehicle count data,
  • smart-card data, etc.
  • Real-time data will be available as well, thanks to a collaboration with a major European provider
    of real-time travel information services.

The postdoc will be involved in activities related to the implementation of the prototype of the PROMENADE platform, which relies on IoT lambda/kappa architectures for automated deployment, scaling, and management of smart mobility resilience-related micro-services, based on the preliminary work from the team.

She/He will also be involved in the development of services for mobility data collection, analysis and decision making.

Please do not hesitate to contact me or Pr. Nour-Eddin EL FAOUZI (nour-eddin.elfaouzi@ifsttar.fr), in CC, for more information, and share CVs of any contact potentially interested to our proposal.

We are ready to schedule a Skype interview to discuss more about the available position.

The postdoctoral position is expected to start in the period July-December 2019 and
could have a duration between 12 and 18 months.

The link to the full call:
https://promenade.licit-lyon.eu/documents/call_for_postdoc_PROMENADE_short.pdf

A postdoctoral position is available within the Team 1 ‘Communicable Diseases Surveillance and Modelling’ of the Pierre Louis Institute of Epidemiology and Public Health (IPLESP) part of INSERM. The candidate is expected to work within the framework of the project NoCOV funded by the ANR Flash COVID-19 with the aim of analyzing the spreading of the epidemic in the French population.

Candidate profile
We are looking for a strongly motivated person with excellent skills in computational modeling, data collection and analysis, and a keen interest in multidisciplinary research. The candidate should have a PhD (or expect to have one for the starting date) in quantitative science, such as physics, applied mathematics, computer science, epidemiology or any close related discipline. Proven ability to work independently and to quickly adapt to new scientific environments are essential for this position. Good communicative skills to successfully collaborate with the other members of the group, and a good knowledge of both oral and written English are required.

About the position
The selected candidate will work in Paris (France). She/he will join the Networks in Disease Ecology group part of IPLESP and will work under the supervision of Dr. Chiara Poletto, and in collaboration with the Team 1 and the Surveillance group responsible for GP COVID-19 surveillance in France.

The topics of the work will be marked by the objectives of NoCOV, which include the mechanistic modeling of COVID spatial spread in the French territory through a computational approach the analysis of surveillance data and the exploration of the possible intervention scenarios. Research tasks will be computational programming (development of data-driven models, agent-based approaches), the analysis and characterization of simulation output and their comparison with incidence data. Experience with data-intensive computational modeling, and data analysis is highly desirable.

The position is full-time and fixed-term available for one year in the first instance.

Applications
Applications will be continuously received and evaluated until the position is filled. Applications should be submitted to Dr. Chiara Poletto via email (chiara.poletto_at_inserm.fr) and must include:

 letter of motivation;
 CV including the list of publications;
 up to 3 selected preprints/publications most relevant for this position;
 Contact details for 2 referees.

Call for PhD applications in the framework of the Labex MME-DII
Opinion Dynamics models: Heterogeneous agents in heterogeneous media.

Description:

The ubiquity of communication devices in our everyday activities, changes the way in which we interact with each other, for example, by allowing very different people, who would not have been able to share a discussion or support a common cause before, to converge on a particular action. These changes seem to affect the very notion of social interaction.

Furthermore, an increasing number of our common actions leave digital traces that are collected by different kinds of agents such as governments, scientific societies, commercial firms and NGOs, etc. The fact that the activities of human society can be massively monitored and stored is a new feature in history, and the impact of this fact on our behavior is far from trivial.

The present pandemic crisis provides an unexpected large scale terrain to study the modifications that arrive when the dynamics of social relations is suddenly modified. Regarding public opinion, it is therefore essential to understand the rules that govern its formation and diffusion, according to the different channels that connect the individuals in the society.

Opinion formation, a recurrent subject of study in social sciences, is often addressed through statistical analyses of data collected by the means of surveys or polls. By following the evolution of the statistical outcomes over time, it is possible to obtain some information about the dynamical processes underlying this phenomenon.

What we call the opinion of a society is a global property that characterises the society as a whole and has emerged from the repeated interactions among their agents. Defined in this way, opinion may be studied statistically. This approach is the mathematical realization of the ideas introduced more than one century ago by E. Durkheim[1] , who coined the notion of social fact. This concept refers to a property characterizing the whole society instead of the individuals, which emerges as an outcome of the dynamics governed by the interactions among them. Once the social fact is created, it is imposed on the members of the society who will find it very difficult to change it.

Early opinion dynamics studies mainly assumed a fully mixed population, which means that every agent may potentially interact with any other in the population, in other words, the interactions among agents were supposed to be long range. This approach, equivalent to a mean-field approximation, neglects the structure of the interactions. However, if social opinion emerges from the interactions between the agents, their structure may be relevant.

In fact, the key role of interactions had been recognized long ago by social scientists, who collected detailed data about social interactions in very small societies, using graph theory to represent them. J. Scott [2] gives a nice historical overview of network development in social sciences. With the development computing power and of Network Theory different models of social interactions have been proposed by physicists, applied mathematicians and computer scientists, thus setting a bridge between two different scientific communities[3] .

In spite of all these efforts studies taking into account the fact that the members of a society are intrinsically heterogeneous and so are their interactions are rare. In a very recent work [4] we have shown that when agent’s heterogeneities are taken into account, the outcomes of the very well-known Hagselmann-Krause dynamics are drastically changed.

In this project the selected candidate, will study the influence of heterogeneity in agents’ properties, in their interaction network, and the interplay between them. To do so she/he will combine both theoretical and data based models, and will apply concepts and methods issued from the study of Dynamical Systems, Statistical Mechanics, Agent Based Models and Network Theory.

References
1. Durkheim, E. Les Règles de la Méthode Sociologique (Les Presses universitaires Paris, France, 1967). URL http://classiques.uqac.ca/classiques/Durkheim_emile/regles_methode/regles_methode.html.
2. Scott, J. Social network analysis: developments, advances, and prospects. SOCNET 1, 21–26 (2011).
3. Castellano, C., Fortunato, S. & Loreto, V. Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591–646 (2009).
4. Schawe H. Hernández L, When open mindedness hinders consensus. Nature SciRep (to be published 2020), arXiv:2001.06877.

Working environment and conditions

The selected candidate will work under the supervision of Dr Laura Hernández at the Laboratoire de Physique Théorique et Modélisation (LPTM) UMR8089 CNRS-CYU, Paris-Seine University, France. She/he will also integrate an international and interdisciplinary team, composed by physicists, computer scientists, linguists, and human scientists, which are part of the OpLaDyn project, http://project.u-cergy.fr/~opladyn/on-going-projects/ awardee of the 4th round of the TransAtlantic Platform, Digging into Data Challenge. https://diggingintodata.org/awards/2016/news/winners-round-four-t-ap-digging-data-challenge , and will benefit of its activities.

The contract should start on September 2020 for a duration of 3 years. The basic gross salary (before taxes) is 1758 €/month. A teaching assistant mission may be assigned which involves 64h/year teaching assistant duties (typically one course each semester), and involves a supplement to the basic salary.

Requirements

Interested candidates should hold a Master in Physics or Mathematics. The application of those completing their master in 2019-2020, is possible provided they graduate before September 2020.

Applicants are expected to have modeling and programming skills, and a marked interest in both theoretical modeling and data analysis.

This is a competitive program, so high records are expected, in particular a good knowledge of Dynamical systems theory, Statistical Physics of phase transitions, and Network Theory would be highly appreciated.

The application should contain:

  • A curriculum vita.
  • An official certificate with the marks obtained at the Bachelor and the Master. Applicant who have not completed the master yet should provide a certified list of the marks obtained until now.
  • A statement of purpose, explaining the applicant’s interest in the project
  • Two recommendation letters of her/his professors or internship supervisors.

These documents should be sent by e-mail to Dr Laura Hernández before May 14th 2020 to: Laura.Hernandez_at_cyu.fr

MULTITOUT : Concilier des objectifs de rendements durables des pêches avec des objectifs écosystémiques : points de référence multi-spécifiques de gestion des pêches et méthodes d’évaluation de scénarios de gestion multi-objectifs.

Résumé : 

La Politique Commune des Pêches d’un côté et la Directive Cadre Stratégie pour le Milieu Marin de l’autre, imposent d’atteindre conjointement des rendements maximaux soutenables pour l’ensemble des espèces capturées et un bon état écologique de l’écosystème marin. Prenant en considération la multiplicité des usages marins et les occupations spatiales variables dans l’espace et dans le temps des ressources et des usages, la planification spatiale marine est un outil incontournable pour l’atteinte de ces objectifs. Pourtant, le système actuel de réglementation des pêches reste principalement basé sur une gestion monospécifique par TAC qui ne garantit pas d’atteindre conjointement tous ces objectifs dans un contexte de pêcherie mixte. Pour basculer de manière opérationnelle dans une approche de gestion écosystémique, il est nécessaire de développer des points de référence et des règles de contrôle des captures spatiales, saisonnières, plurispécifiques et pluriflottilles. L’objet de cette thèse est de 1) développer un cadre théorique de développement de ces points de référence plurispécifiques et de ces nouvelles HCR spatiales et saisonnières, 2) de le valider avec un modèle de simulation de pêcherie, 3) de le tester sur la pêcherie mixte démersale du golfe de Gascogne et 4) de l’exploiter pour simuler des scénarios de gestion.

Mots Clés : indicateurs multi-dimensionnels, optimisation multi-critères, points de référence, règles de gestion, scénarios, dynamique spatiale et saisonnière, pêche, golfe de Gascogne, ISIS-Fish

Profil de candidature souhaité : Master avec des compétences en modélisation, statistiques, optimisation, halieutique, écologie numérique

Merci d’adresser votre candidature à Stéphanie Mahévas (smahevas_at_ifremer.fr) et Sigrid Lehuta (slehuta@ifremer.fr).

The Inria ARAMIS Lab at the Paris Brain Institute announces several PhD scholarships funded by the European Research Council (ERC).

We welcome applications to work on the theoretical development of network science methods (Multilayer networks, Temporal networks, Network controllability), as well as application oriented data-driven research in neuroimaging and neuroscience.

The ARAMIS Lab is highly interdisciplinary, hosting PhD students, postdocs and engineers with diverse backgrounds, including physics, mathematics, computer science, statistics, psychology and neuroscience.

Scholarships are fully funded for 3 years. Scientific education, professional formations, as well as generous travels awards, are available throughout the PhD. Successful applicants will start from October 2020.

Available subjects
Multilayer networks, Temporal networks, Network controllability

Project Summary
Our group is looking for PhD students in the area of network science. Our current work
spans the development of network approaches for understanding brain functioning,
characterizing neurological diseases, and discovering predictive biomarkers.

Basic Qualifications
We seek students motivated to explore the complexity of biological systems from a
network viewpoint. The ideal candidate has a physics, mathematics, computer science or
statistics MS. Familiarity with network science and neuroscience/imaging is expected.

Application Instructions
Prospective students should submit an application consisting of i) a current CV with
university grades list, ii) a brief statement of research experience and interests, and iii)
one letter of recommendation sent by the writer to: fabrizio.de-vico-fallani_at_inria.fr

+ Info : https://sites.google.com/site/devicofallanifabrizio/positions